Veterinary clinical pathology最新文献

Background: Lipoprotein fractions are reported to be unstable in stored human samples, and there is a paucity of information on the analytical precision of electrophoretic separation of lipoproteins in canine serum samples.

Objective: The aim of this study was to assess the effects of intra- and inter-assay imprecision and of storage conditions on the electrophoretic separation of canine lipoproteins.

Methods: Imprecision was assessed by calculating the coefficient of variation (CV) of five replicates of six serum samples run in two sequential runs of agarose gel lipoprotein electrophoresis. The effect of storage was assessed with a Friedmann test by comparing the results of samples analyzed after sampling (T0) and after 24 and 48 h at room temperature or stored at 4°C and after 7, 14, 21 days, 1, 2, and 3 months at -20°C or at -80°C. Moreover, electrophoretograms obtained after storage were visually analyzed by two observers in a blind manner to assess whether storage alters the electrophoretic profile.

Results: The imprecision of high-density lipoproteins (HDL), low-density lipoproteins (LDL), very low-density lipoprotein (VLDL), and chylomicrons were respectively 0.8%-11.5%, 2.4%-22.7%, 2.3%-11.5%, and 12.5%-105.2%. Compared with T0, HDL significantly decreased, and LDL significantly increased over time in all the storage conditions, whereas VLDL significantly increased only in frozen samples, and chylomicrons did not significantly differ. In frozen samples, deviations from baseline values were lower than the imprecision of the method, and visual misclassifications of electrophoretograms were rare.

Conclusions: Despite minimal variation in the percentage of some fractions, freezing does not influence the interpretation of canine lipidograms.

A 6-year-old, intact male English Bulldog presented for evaluation of weakness. Physical examination revealed mildly pale mucous membranes and ophthalmologic alterations. A complete blood cell count was performed on the Urit Smart V5 and the Sysmex XN-1000V, and showed several hematologic discrepancies between the analyzers. The Urit reported marked leukocytosis and thrombocytosis, and the Sysmex reported marked thrombocytosis on the impedance and optical RET channel but mild thrombocytopenia on the optical PLT-F channel. On the blood smear examination, there was an abundant proteinaceous material compatible with cryoglobulins. The red blood cell exhibited a notable degree of poikilocytosis. The number of leukocytes was found to be correlated with the Sysmex total white blood cell count, while the platelet count demonstrated a better correlation with the optical PLT-F channel from the Sysmex. Centrifugation of the blood in a capillary tube (at room temperature) identified a cryoglobulin precipitate. Additionally, the blood was reanalyzed soon after warming it to 37°C, which partially corrected most of the interferences. The serum protein electrophoresis demonstrated a marked hyperproteinemia with mild hypoalbuminemia and a marked hypergammaglobulinemia. On diagnostic imaging, a mild splenomegaly was noted. Cytology of the spleen showed Leishmania infantum infection, plasma cell hyperplasia, and extramedullary hematopoiesis. This is the first description of a dog infected with Leishmania. infantum with concurrent monoclonal hypergammaglobulinemia and cryoglobulinemia. We present a detailed description of the interference of cryoglobulin with the Urit Smart V5 and the Sysmex XN-1000V, along with the usefulness of Sysmex PLT-F in this condition.

Objective: In this study, we aim to determine if machine learning can reduce manual smear review (MSR) rates while meeting or exceeding the performance of traditional MSR criteria.

Method: 9938 automated CBCs with paired MSRs were performed on samples from rhesus and cynomolgus macaques. The definition of a positive (abnormal) smear was determined. Two expert-derived MSR criteria were created: criteria adapted from published, standardized human laboratory criteria (Adapted International Consensus Guidelines[aICG]) and internally generated criteria (Center Consensus Guidelines [CCG]). An ensemble machine learning model was trained on an independent subset of the data to optimize the balanced accuracy of classification, a combined measure of sensitivity and specificity. The resulting machine learning model and the two expert-derived MSR criteria were applied to a test dataset, and their performance compared.

Results: aICG criteria demonstrated high sensitivity (80.8%) and MSR rate (74.2%) while CCG criteria demonstrated lower sensitivity (57.1%) and MSR rate (36.1%). The machine learning model integrated with CCG criteria had a superior combination of both sensitivity (76.8%) and MSR rate (45.1%) achieving a false negative rate of 1.6%.

Conclusion: Machine learning in combination with expert-derived criteria can optimize the selection of samples for MSR thus decreasing MSR rates and labor efforts required for CBC performance.

Artificial intelligence (AI) has transformative potential in veterinary pathology in tasks ranging from cell enumeration and cancer detection to prognosis forecasting, virtual staining techniques, and individually tailored treatment plans. Preclinical testing and validation of AI systems (AIS) are critical to ensure diagnostic safety, efficacy, and dependability. In this two-part series, challenges such as the AI chasm (ie, the discrepancy between the AIS model performance in research settings and real-world applications) and ethical considerations (data privacy, algorithmic bias) are reviewed and underscore the importance of tailored quality assurance measures that address the nuances of AI in veterinary pathology. This review advocates for a multidisciplinary approach to AI development and implementation, focusing on image-based tasks, highlighting the necessity for collaboration across veterinarians, computer scientists, and ethicists to successfully navigate the complex landscape of using AI in veterinary medicine. It calls for a concerted effort to bridge the AI chasm by addressing technical, ethical, and regulatory challenges, facilitating AI integration into veterinary pathology. The future of veterinary pathology must balance harnessing AI's potential while intentionally mitigating its risks, ensuring the welfare of animals and the integrity of the veterinary profession are safeguarded. Part I of this review focuses on considerations for model development, and Part II focuses on external validation of AI.

Background: Basophils are the rarest blood leukocyte in most healthy domestic mammals and the clinical significance of basophilia is poorly understood.

Objectives: To empirically determine magnitude thresholds for basophilia, identify its hematologic correlates, and identify associations between breeds, specific diseases, disease categories, organ systems, and basophilia in dogs, cats, and horses.

Methods: CBCs and clinical information from dogs, cats, and horses were collected from the University of California-Davis School of Veterinary Medicine between 2000 and 2020. Relationships between basophil concentration and other CBC parameters were evaluated by computing Pearson's correlation (r). Magnitude thresholds for basophilia (ie, mild, moderate, severe) were determined by evaluating the distribution of basophil counts for each species. For severe cases of basophilia, the clinical diagnoses were categorized by the organ system affected and the underlying pathomechanism. Basophilia groups were compared to a time-matched, randomly selected control group, and chi-square analyses were performed to evaluate associations with disease.

Results: A total of 143 841 (dog), 32 576 (cat), and 44 887 (horse) CBCs were collected. For all three species, basophilia was over-represented in some breeds. Basophilia was associated with respiratory disease in both dogs and cats. In dogs, lymphoma and mast cell neoplasia were associated with basophilia. In horses, an increased incidence of basophilia was not associated with any disease category or organ system.

Conclusions: This is the largest study evaluating the hematologic correlations and disease associations with basophilia in dogs, cats, and horses. While basophilia was reported alongside many diseases, certain associations may aid clinicians in narrowing down underlying causes.

Background: Canine vector-borne diseases (VBDs) induce non-specific dysproteinemias, detectable by serum protein electrophoresis (SPE). VBDs have been reported to induce a monoclonal gammopathy pattern. Monoclonal gammopathies are commonly the result of paraprotein (M-protein) produced by an immunoglobulin-secreting neoplasm.

Objectives: The aims of this study were to characterize and compare SPE and immunofixation (IF) changes, evaluate the performance of previously identified SPE and IF interpretative criteria, and identify M-proteins in a cohort of dogs seropositive for a VBD and with an unknown history for an immunoglobulin-secreting neoplasm.

Methods: A total of 143 serum samples from dogs that tested seropositive for different vector-borne pathogens were assessed by SPE. Cases with abnormal globulin fractions were further characterized by IF. Protein fraction and IF labeling results were evaluated using Kruskal-Wallis with Dunn's multiple comparisons and principal component analysis (PCA).

Results: IF was performed in 112 VBD-seropositive samples with dysproteinemia evaluated by SPE. Most (84/112, 75%) had a polyclonal expansion. Only two dogs had findings suggestive of an M-protein when considering both SPE and immunofixation. PCA clustered E.canis/A.phagocytophilum and B.gibsoni/CM.haematoparvum groups with relatively more γ-globulins than albumin and α-globulins, and the B.gibsoni/CM.haematoparvum group with more prominent IgA and IgM labeling than IgG labeling. Additionally, D.immitis clustered with more prominent β-globulins than γ-globulins and more IgG4 than IgG-FC.

Conclusions: The previously derived interpretative criteria suggested an M-protein in very few VBD-seropositive dogs. PCA identified SPE and immunofixation pattern differences between dogs seropositive for different infectious agents.